We propose a nonparametric, kernel-based joint estimator for conditional mean and covariance matrices in large unbalanced panels. Our estimator, with proven consistency and finite-sample guarantees, is applied to a comprehensive panel of monthly US stock excess returns from 1962 to 2021, conditioned on macroeconomic and firm-specific covariates. The estimator captures time-varying cross-sectional dependencies effectively, demonstrating robust statistical performance. In asset pricing, it generates conditional mean-variance efficient portfolios with out-of-sample Sharpe ratios that substantially exceed those of equal-weighted benchmarks.

翻译：我们提出了一种基于核函数的非参数联合估计方法，用于大型非平衡面板中的条件均值与协方差矩阵估计。该估计量具有经证明的一致性与有限样本保证，我们将其应用于1962年至2021年美国股票月度超额收益的综合性面板数据，并以宏观经济及公司特定协变量为条件。该估计方法能有效捕捉时变的横截面依赖关系，展现出稳健的统计性能。在资产定价应用中，基于该方法构建的条件均值-方差有效投资组合，其样本外夏普比率显著超越等权重基准组合的表现。